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Dr. Anne Gehre
Email: anne.gehre@cs.rwth-aachen.de

http://annegehre.de/



Publications


Feature Curve Co-Completion in Noisy Data


Anne Gehre, Isaak Lim, Leif Kobbelt
Computer Graphics Forum (Proc. EUROGRAPHICS 2018)
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Feature curves on 3D shapes provide important hints about significant parts of the geometry and reveal their underlying structure. However, when we process real world data, automatically detected feature curves are affected by measurement uncertainty, missing data, and sampling resolution, leading to noisy, fragmented, and incomplete feature curve networks. These artifacts make further processing unreliable. In this paper we analyze the global co-occurrence information in noisy feature curve networks to fill in missing data and suppress weakly supported feature curves. For this we propose an unsupervised approach to find meaningful structure within the incomplete data by detecting multiple occurrences of feature curve configurations (co-occurrence analysis). We cluster and merge these into feature curve templates, which we leverage to identify strongly supported feature curve segments as well as to complete missing data in the feature curve network. In the presence of significant noise, previous approaches had to resort to user input, while our method performs fully automatic feature curve co-completion. Finding feature reoccurrences however, is challenging since naive feature curve comparison fails in this setting due to fragmentation and partial overlaps of curve segments. To tackle this problem we propose a robust method for partial curve matching. This provides us with the means to apply symmetry detection methods to identify co-occurring configurations. Finally, Bayesian model selection enables us to detect and group re-occurrences that describe the data well and with low redundancy.

» Show BibTeX

@inproceedings{gehre2018feature,
title={Feature Curve Co-Completion in Noisy Data},
author={Gehre, Anne and Lim, Isaak and Kobbelt, Leif},
booktitle={Computer Graphics Forum},
volume={37},
number={2},
year={2018},
organization={Wiley Online Library}
}





Interactive Curve Constrained Functional Maps


Anne Gehre, Michael Bronstein, Leif Kobbelt, Justin Solomon
Eurographics Symposium on Geometry Processing 2018
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Functional maps have gained popularity as a versatile framework for representing intrinsic correspondence between 3D shapes using algebraic machinery. A key ingredient for this framework is the ability to find pairs of corresponding functions (typically, feature descriptors) across the shapes. This is a challenging problem on its own, and when the shapes are strongly non-isometric, nearly impossible to solve automatically. In this paper, we use feature curve correspondences to provide flexible abstractions of semantically similar parts of non-isometric shapes. We design a user interface implementing an interactive process for constructing shape correspondence, allowing the user to update the functional map at interactive rates by introducing feature curve correspondences. We add feature curve preservation constraints to the functional map framework and propose an efficient numerical method to optimize the map with immediate feedback. Experimental results show that our approach establishes correspondences between geometrically diverse shapes with just a few clicks.

» Show BibTeX

@article{Gehre:2018:InteractiveFunctionalMaps,
author = "Gehre, Anne and Bronstein, Michael and Kobbelt, Leif and Solomon, Justin",
title = "Interactive Curve Constrained Functional Maps",
journal = "Computer Graphics Forum",
volume = 37,
number = 5,
year = 2018
}





Adapting Feature Curve Networks to a Prescribed Scale


Anne Gehre, Isaak Lim, Leif Kobbelt
Computer Graphics Forum (Proc. EUROGRAPHICS 2016)
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Feature curves on surface meshes are usually defined solely based on local shape properties such as dihedral angles and principal curvatures. From the application perspective, however, the meaningfulness of a network of feature curves also depends on a global scale parameter that takes the distance between feature curves into account, i.e., on a coarse scale, nearby feature curves should be merged or suppressed if the surface region between them is not representable at the given scale/resolution. In this paper, we propose a computational approach to the intuitive notion of scale conforming feature curve networks where the density of feature curves on the surface adapts to a global scale parameter. We present a constrained global optimization algorithm that computes scale conforming feature curve networks by eliminating curve segments that represent surface features, which are not compatible to the prescribed scale. To demonstrate the usefulness of our approach we apply isotropic and anisotropic remeshing schemes that take our feature curve networks as input. For a number of example meshes, we thus generate high quality shape approximations at various levels of detail.

» Show BibTeX

@inproceedings{gehre2016adapting,
title={Adapting Feature Curve Networks to a Prescribed Scale},
author={Gehre, Anne and Lim, Isaak and Kobbelt, Leif},
booktitle={Computer Graphics Forum},
volume={35},
number={2},
pages={319--330},
year={2016},
organization={Wiley Online Library}
}





Identifying Style of 3D Shapes using Deep Metric Learning


Isaak Lim, Anne Gehre, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2016
pubimg

We present a method that expands on previous work in learning human perceived style similarity across objects with different structures and functionalities. Unlike previous approaches that tackle this problem with the help of hand-crafted geometric descriptors, we make use of recent advances in metric learning with neural networks (deep metric learning). This allows us to train the similarity metric on a shape collection directly, since any low- or high-level features needed to discriminate between different styles are identified by the neural network automatically. Furthermore, we avoid the issue of finding and comparing sub-elements of the shapes. We represent the shapes as rendered images and show how image tuples can be selected, generated and used efficiently for deep metric learning. We also tackle the problem of training our neural networks on relatively small datasets and show that we achieve style classification accuracy competitive with the state of the art. Finally, to reduce annotation effort we propose a method to incorporate heterogeneous data sources by adding annotated photos found online in order to expand or supplant parts of our training data.

» Show BibTeX

@article{Lim:2016:StyleLearning,
author = "Lim, Isaak and Gehre, Anne and Kobbelt, Leif",
title = "Identifying Style of 3D Shapes using Deep Metric Learning",
journal = "Computer Graphics Forum",
volume = 35,
number = 5,
year = 2016
}





Geodesic Iso-Curve Signature


Anne Gehre, David Bommes, Leif Kobbelt
21st International Symposium on Vision, Modeling and Visualization (VMV 2016)
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During the last decade a set of surface descriptors have been presented describing local surface features. Recent approaches have shown that augmenting local descriptors with topological information improves the correspondence and segmentation quality. In this paper we build upon the work of Tevs et al. and Sun and Abidi by presenting a surface descriptor which captures both local surface properties and topological features of 3D objects. We present experiments on shape repositories that are provided with ground-truth correspondences (FAUST, SCAPE, TOSCA) which show that this descriptor outperforms current local surface descriptors.

» Show BibTeX

@INPROCEEDINGS{gbk2016,
author = {Gehre, Anne and Bommes, David and Kobbelt, Leif}
title = {Geodesic Iso-Curve Signature},
booktitle = {Vision, Modeling {\&} Visualization},
year = {2016},
publisher = {The Eurographics Association}
}





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